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Extract temporally-varying coefficients from a fitted model

Source: R/temporal_tvc.R
tvc.Rd

Extract posterior distributions of temporally-varying coefficients (TVCs) from a fitted tulpa model with TVC specification.

Usage

tvc(object, terms = NULL, summary = FALSE, probs = c(0.025, 0.5, 0.975), ...)

# S3 method for class 'tulpa_fit'
tvc(object, terms = NULL, summary = FALSE, probs = c(0.025, 0.5, 0.975), ...)

Arguments

object

A tulpa_fit object fitted with tvc argument

terms

Which TVC terms to extract. If NULL (default), extracts all.

summary

Logical; if TRUE, return summary statistics instead of full posterior draws.

probs

Quantiles to compute if summary = TRUE.

...

Ignored

Value

A tulpa_tvc_posterior object containing:

  • draws: Array of posterior draws (draws x times x terms)

  • time_levels: Time point labels

  • term_names: Names of TVC terms

See also

temporal_tvc(), plot.tulpa_tvc_posterior()

Examples

# \donttest{
set.seed(160)
n_t <- 10L; reps <- 5L
walk <- cumsum(rnorm(n_t, 0, 0.35)); walk <- walk - mean(walk)
year <- rep(seq_len(n_t), each = reps)
df <- data.frame(year = year, x = rnorm(length(year)))
df$count <- rpois(nrow(df), exp(0.3 + (0.5 + walk[year]) * df$x))

# The slope on `x` walks in time; TVC is exact-mode only.
fit <- tulpa(
  count ~ x,
  data = df,
  family = "poisson",
  temporal = temporal_tvc("year", terms = ~ x - 1, structure = "rw1"),
  mode = "exact",
  control = list(n_iter = 200L, n_warmup = 100L, seed = 1L)
)

tvc_post <- tvc(fit)
summary(tvc_post)
plot(tvc_post, "x")
# }